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Quadratic Phase Coupling Detection in Harmonic Vibrations via an Order-Recursive AR Bispectrum Estimation

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Abstract

The paper proposes a bispectral scheme for quadratic phase-coupling (QPC) detection in harmonic signals in white and colored noise, possibly generated by non-linear vibrations. This scheme is carried out via an autoregressive bispectrum (AR) using several criteria to fix an optimum order for the AR model used. First, we propose a recursive-in-order algorithm for AR model-parameter calculation in the bispectrum estimation problem. This algorithm is based on the recursion-in-order minimization of appropriate squared errors with respect to the reflection coefficients, introduced by the Levinson recursion for Toeplitz and non-Hermitian matrices. The recursive nature in this method allows us to obtain the bispectrum of several orders up to the desired one, with significant computational savings. In computer simulations this method demonstrates its potential both in the estimation of signal bispectra and in QPC bispectral detection problems in noisy environments.

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References

  1. Nikias, C. L. and Petropulu, A. P., Higher-Order Spectra Analysis. A Nonlinear Signal Processing Framework, Prentice Hall, Englewood Cliffs, NJ, 1993.

    Google Scholar 

  2. Nikias, C. L. and Raghuveer, M., ‘Bispectrum estimation: A digital signal processing framework’, Proceedings of the IEEE 75, 1985, 869–891.

    Google Scholar 

  3. Nikias, C. L. and Mendel, J. M., ‘Signal processing with higher-order spectra’, IEEE Signal Processing Magazine 7, 1993, 10–37.

    Google Scholar 

  4. Swami, A., Giannakis, G. B., and Zhou, G., ‘Bibliography on higher-order statistics’, Signal Processing 60(1), 1997, 65–126.

    Google Scholar 

  5. Elgar, S., Van Atta, C. W., and Gharib, M., ‘Bispectral analysis of ordered and chaotic vortex shedding from vibrating cylinders’, Physica D 39, 1989, 281–286.

    Google Scholar 

  6. Shimizu, H. and Inoue, T., ‘Machine fault diagnosis by vibrational analysis. Exploratory introduction of the bispectrum method’, Bulletin of the Faculty of Engineering, Yokohama National University 27, 1978, 52–60.

    Google Scholar 

  7. Barker, R. W., Hinich, M. J., and Klutke, G. A., ‘Higher-order statistics and spectral estimation for vibration signal pattern recognition’, in Proceedings of the International Workshop on Higher-Order Statistics, Chamrousse, France, July, 1991, pp. 183–186.

  8. Collis, W. B., White, P., and Hammond, J., ‘Bispectrum and trispectrum of mechanical systems’, in Proceedings of the International Workshop on Higher-Order Statistics, Begur-Gerona, Spain, June, 1995, pp. 124–128.

  9. Bendat, J. S. and Piersol, A. G., ‘Spectral analysis of nonlinear systems involving square-low operations’, Journal of Sound and Vibration 81, 1982, 199–213.

    Google Scholar 

  10. Lutes, L. D., ‘Trispectrum for the response of a non-linear oscilator’, International Journal of Non-Linear Mechanics 26(2), 1991, 893–909.

    Google Scholar 

  11. Kim, Y. C., Beall, J. M., Powers, E. J., and Miksad, R. W., ‘Bispectrum and nonlinear wave coupling’, Physics of Fluids 23, 1980, 258–263.

    Google Scholar 

  12. Wu, D. W. and Liu, C. R., ‘An analytical model of cutting dynamics, Parts I and II’, Transactions of the American Society of Mechanical Engineering, Journal of Engineering for Industry, 1985, 107–118.

  13. Billings, S. A. and Tsang, K. N., ‘Spectral analysis for non-linear systems, Part I: Parametric non-linear spectral analysis’, Mechanical Systems and Signal Processing 3(4), 1991, 319–339.

    Google Scholar 

  14. Billings, S. A. and Tsang, K. N., ‘Spectral analysis for non-linear systems, Part III: Case study examples’, Mechanical Systems and Signal Processing 4(1), 1991, 3–21.

    Google Scholar 

  15. Balachandran, B. and Khan, K., ‘Spectral analyses of nonlinear interactions’, Mechanical Systems and Signal Processing 10(6), 1996, 711–727.

    Google Scholar 

  16. Huber, P. J., Kleiner, B., Gasser, T., and Dummermuth, G., ‘Statistical methods for investigation phase relations in stationary stochastic processes’, IEEE Transactions on Audio Electroacoustic AU-19, 1971, 78–86.

    Google Scholar 

  17. Sato, T., Sasaki, K., and Nakamura, Y., ‘Real-time bispectral analysis of gear noise and its applications to contactless diagnosis’, Journal of the Acoustical Society of America 62, 1977, 382–387.

    Google Scholar 

  18. Sato, T., Kishimoto, T., and Sasaki, K., ‘Laser Doppler particle measuring system using nonsinusoidal forced vibration and bispectral analysis’, Applied Optics 17, 1978, 667–670.

    Google Scholar 

  19. Kim, Y. C. and Powers, E. J., ‘Digital bispectral analysis and its applications to nonlinear wave interactions’, IEEE Transactions on Plasma Science 7, 1979, 120–131.

    Google Scholar 

  20. Raghuveer, M. and Nikias, C. L., ‘Bispectrum estimation: A parametric approach’, IEEE Transactions on Acoustic, Speech and Signal Processing 33, 1985, 1213–1230.

    Google Scholar 

  21. Raghuveer, M. and Nikias, C. L., ‘Bispectrum estimation via AR modeling’, Signal Processing 9(1), 1986, 35–48.

    Google Scholar 

  22. Kim, Y. C., Beall, J. M., Powers, E. J., and Miksad, R. W., ‘Bispectrum and nonlinear wave coupling’, Physics of Fluids 23(2), 1980, 258–263.

    Google Scholar 

  23. Gallego, A., Carrión, M. C., Ruiz, D. P., and Medouri, A., ‘Prewindowed and postwindowed methods for bispectrum estimation via AR modelling’, IEE Electronics Letters 29(2), 1993, 181–182.

    Google Scholar 

  24. Gallego, A., Carrión, M. C., Ruiz, D. P., and Medouri, A., ‘The relationship between AR-modelling bispectral estimation and the theory of linear prediction’, Signal Processing 37(3), 1994, 381–388.

    Google Scholar 

  25. Makhoul, J., ‘Linear prediction: A tutorial review’, Proceedings of the IEEE 63(4), 1975, 561–580.

    Google Scholar 

  26. Zohar, S., ‘The solution of a Toeplitz set of linear equations’, Journal of the Association for Computing Machinery 21, 1974, 272–276.

    Google Scholar 

  27. Marple, S. L., Digital Spectral Analysis with Applications, Prentice Hall, Englewood Cliffs, NJ, 1987.

    Google Scholar 

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Gallego, A., Urdiales, C. & Ruiz, D.P. Quadratic Phase Coupling Detection in Harmonic Vibrations via an Order-Recursive AR Bispectrum Estimation. Nonlinear Dynamics 19, 273–294 (1999). https://doi.org/10.1023/A:1008398026872

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  • DOI: https://doi.org/10.1023/A:1008398026872

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